# Read in a tx2gene file with transcript identifiers in the first column and gene identifiers in the second column
tx2gene <- hsdb[, c("tx_id", "gene_id")]
# Run tximport
txi_file <- "data/txi.day8.RDS"
if (!rebuild_rds & file.exists(txi_file)){
txi <- readRDS(txi_file)
}else{
files <- files[rownames(meta)]
txi <- tximport(files,
type = "salmon",
tx2gene = tx2gene,
countsFromAbundance = "lengthScaledTPM",
ignoreTxVersion = FALSE)
saveRDS(txi, txi_file)
}
# Look at the counts
class(txi)
## [1] "list"
attributes(txi)
## $names
## [1] "abundance" "counts" "length"
## [4] "countsFromAbundance"
txi$counts %>% View()
When you have annotations that are from a different source from your reference you can run into problems (i.e lose genes). Some checks you can do before proceeding:
dim(txi$counts)tximport() you will get a message in your console. If you see something like transcripts missing from tx2gene start troubleshooting.dim(txi$counts)
## [1] 58735 22
It is always a good idea to check if:
### Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))
## [1] TRUE
### Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))
## [1] TRUE
### Check that all samples are in the same order
meta <- meta[colnames(txi$counts),]
all(colnames(txi$counts) == rownames(meta))
## [1] TRUE
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
Here we subset protein coding genes.
## Create DESeq2Dataset object
dds_file <- "data/dds.day8.RDS"
meta$treatment <- as.factor(meta$treatment)
meta$response <- as.factor(meta$response)
meta$er <- as.factor(meta$er)
meta$date_of <- as.factor(meta$date_of)
meta$tumor_percentage <- as.factor(meta$tumor_percentage)
meta$tumor_percentage_high <- as.factor(meta$tumor_percentage_high)
if (remove_cases_2_19){
non_responders <- meta %>% dplyr::filter(study_id %in% c(2, 19)) %>% row.names()
}
if (!rebuild_rds & file.exists(dds_file)){
dds <- readRDS(dds_file)
}else{
dds <- DESeqDataSetFromTximport(txi,
colData = meta,
design = ~response)
if (remove_cases_2_19){
dds <- dds[,!colnames(dds) %in% non_responders]
}
design(dds) <- formula(~response + er + tumor_percentage_high + date_of)
# subset protein-coding genes
pc_genes <- intersect(protein_coding_genes$ensembl_gene_id, row.names(dds))
dds <- dds[pc_genes,]
# 100 reads / 20 samples
keep <- rowSums(counts(dds)) >= 100
dds <- dds[keep,]
# Run DESeq2
dds <- DESeq(dds)
saveRDS(dds, dds_file)
}
counts <- counts(dds, normalized = TRUE)
design <- as.data.frame(colData(dds))
degCheckFactors(counts[, 1:20])
res <- results(dds)
degQC(counts, design[["response"]], pvalue = res[["pvalue"]])
degQC(counts, design[["er"]], pvalue = res[["pvalue"]])
degQC(counts, design[["tumor_percentage_high"]], pvalue = res[["pvalue"]])
mdata <- colData(dds) %>% as.data.frame() %>%
dplyr::select(response, er, date_of, tumor_percentage_high)
#resCov <- degCovariates(log2(counts(dds)+0.5), mdata)
mdata %>% ggplot(aes(tumor_percentage_high, fill = response)) + geom_bar(position = "dodge2")
cor <- degCorCov(mdata)
mdata %>% ggplot(aes(date_of, fill = response)) + geom_bar(position = "dodge2")
### Transform counts for data visualization (unsupervised analysis)
rld_file <- "data/rld.day8.RDS"
if (!rebuild_rds & file.exists(rld_file)){
rld <- readRDS(rld_file)
}else{
rld <- rlog(dds, blind = TRUE)
saveRDS(rld, rld_file)
}
class(rld) # what type of object is this
## [1] "DESeqTransform"
## attr(,"package")
## [1] "DESeq2"
# we also need just a matrix of transformed counts
rld_mat <- assay(rld)
# Use the DESeq2 function
plotPCA(rld, intgroup = c("response")) + geom_label_repel(aes(label = name))
# Use the DESeq2 function
plotPCA(rld, intgroup = c("er")) + geom_label_repel(aes(label = name))
# Use the DESeq2 function
plotPCA(rld, intgroup = c("tumor_percentage")) + geom_label_repel(aes(label = name))
# Use the DESeq2 function
plotPCA(rld, intgroup = c("tumor_percentage_high")) + geom_label_repel(aes(label = name))
# Use the DESeq2 function
plotPCA(rld, intgroup = c("date_of")) + geom_label_repel(aes(label = name))
# Correlation matrix
rld_cor <- cor(rld_mat)
meta$study_id <- as.factor(meta$study_id)
# Create annotation file for samples
annotation <- meta[, c("response", "er", "tumor_percentage_high", "date_of")]
# Change colors
heat.colors <- brewer.pal(6, "Blues")
# Plot heatmap
pheatmap(rld_cor,
annotation = annotation,
border = NA,
fontsize = 20)
# Correlation matrix
rld_cor <- cor(rld_mat)
meta$study_id <- as.factor(meta$study_id)
# Create annotation file for samples
annotation <- meta[, c("response", "er", "tumor_percentage_high", "date_of", "study_id")]
# Change colors
heat.colors <- brewer.pal(6, "Blues")
# Plot heatmap
pheatmap(rld_cor,
annotation = annotation,
border = NA,
fontsize = 20)
Gene example
d <- plotCounts(dds,
gene = "ENSG00000130234",
intgroup = "response",
returnData = TRUE)
ggplot(d, aes(x = response, y = count)) +
geom_point(position = position_jitter(w = 0.1, h = 0)) +
geom_text_repel(aes(label = rownames(d))) +
theme_bw(base_size = 10) +
ggtitle("ACE2") +
theme(plot.title = element_text(hjust = 0.5)) +
scale_y_log10()
# Add a column for significant genes
resResponse_tb_vis <- resResponse_tb %>% mutate(threshold = padj < 0.01)
resResponse_tb_vis$symbol <- ifelse((abs(resResponse_tb_vis$log2FoldChange) > 1.5),
resResponse_tb_vis$symbol, NA)
resResponse_tb_vis$symbol <- ifelse(resResponse_tb_vis$threshold,
resResponse_tb_vis$symbol, NA)
fig4b <- ggplot(resResponse_tb_vis,
aes(log2FoldChange, -log10(padj), label = symbol)) +
geom_point(aes(colour = threshold)) +
ggtitle("Response pCR vs non-pCR") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
scale_y_continuous(limits = c(0, 6))+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank()) +
geom_text_repel(aes(label = symbol), size = 5)
saveRDS(fig4b, "data/fig4b.RDS")
fig4b
# Add a column for significant genes
resER_tb <- resER_tb %>% mutate(threshold = padj < 0.01)
ggplot(resER_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("ER: Positive vs Negative") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Add a column for significant genes
resTP_tb <- resTP_tb %>% mutate(threshold = padj < 0.01)
ggplot(resTP_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("Tumor_percentage_high: High vs Low") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Add a column for significant genes
resDO_tb <- resDO_tb %>% mutate(threshold = padj < 0.01)
ggplot(resDO_tb) +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = threshold)) +
ggtitle("Dafe of: 20180323 vs 20180228") +
xlab("log2 fold change") +
ylab("-log10 adjusted p-value") +
scale_x_continuous(limits = c(-10,10)) +
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(1.25)))
# Create a matrix of normalized expression
sig_up <- resResponse_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resResponse_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("response"), drop = FALSE],
main = "Top 50 Up- and Down- regulated genes in Response: pCR vs non-pCR",
fontsize = 20)
# Create a matrix of normalized expression
sig_up <- resER_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resER_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("er"), drop = FALSE],
main = "Top 50 Up- and Down- regulated genes in ER: positive vs negative",
fontsize = 20)
# Create a matrix of normalized expression
sig_up <- resTP_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resTP_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("tumor_percentage_high"), drop = FALSE],
main = "Top Up/Down-regulated genes in Tumor_percentage_high: high vs low",
fontsize = 20)
# Create a matrix of normalized expression
sig_up <- resDO_tb_significant %>% arrange(-log2FoldChange) %>% head(50) %>% pull(gene)
sig_down <- resDO_tb_significant %>% arrange(log2FoldChange) %>% head(50) %>% pull(gene)
sig <- c(sig_up, sig_down)
row_annotation <- gene_symbol %>%
as_tibble() %>%
dplyr::filter(gene_id %in% sig)
plotmat <- txi$abundance[c(sig_up, sig_down),] %>% as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
left_join(gene_symbol, by = c("ensembl_gene_id" = "gene_id")) %>%
drop_na(symbol)
plotmat$ensembl_gene_id <- NULL
plotmat <- plotmat %>% column_to_rownames(var = "symbol") %>% as.matrix()
# Color palette
heat.colors <- brewer.pal(6, "YlOrRd")
# Plot heatmap
pheatmap(plotmat,
scale = "row",
show_rownames = TRUE,
border = FALSE,
annotation = meta[, c("response"), drop = FALSE],
main = "Top 50 Up- and Down- regulated genes in date_of: 20180323 vs 20180228",
fontsize = 20)
bg_genes <- rownames(resResponse)
## Run GO enrichment analysis
compgo_file <- "data/day8.compgo.up.RDS"
if (file.exists(compgo_file)){
compGO <- readRDS(compgo_file)
}else{
compGO <- enrichGO(gene = sigResponse_up,
universe = bg_genes,
keyType = "ENSEMBL",
OrgDb = "org.Hs.eg.db",
ont = "BP",
qvalueCutoff = 0.05,
pAdjustMethod = "BH",
readable = TRUE)
saveRDS(compGO, compgo_file)
}
dotplot(compGO,
showCategory = 20,
title = "GO (Biological Process) Enrichment \n Analysis for UP in pCR)",
label_format = 20,
font.size = 10)
## Output results from GO analysis to a table
print("UP")
## [1] "UP"
results_up <- data.frame(compGO@result) %>% dplyr::filter(p.adjust < 0.05)
nrow(results_up)
## [1] 458
write_csv(results_up, "tables/T20.day8.GO_BP_UP.csv")
compgo_file <- "data/day8.compgo.down.RDS"
if (file.exists(compgo_file)){
compGO <- readRDS(compgo_file)
}else{
compGO <- enrichGO(gene = sigResponse_down,
universe = bg_genes,
keyType = "ENSEMBL",
OrgDb = "org.Hs.eg.db",
ont = "BP",
qvalueCutoff = 0.05,
pAdjustMethod = "BH",
readable = TRUE)
saveRDS(compGO, compgo_file)
}
results_down <- data.frame(compGO@result) %>% dplyr::filter(p.adjust < 0.05)
print("Down")
## [1] "Down"
nrow(results_down)
## [1] 4
write_csv(results_down, "tables/T21.day8.GO_BP_DOWN.csv")
dotplot(compGO,
showCategory = 20,
title = "GO (Biological Process) Enrichment \n Analysis for Down in pCR)",
label_format = 20,
font.size = 10)
bg_genes <- rownames(resResponse)
## Run GO enrichment analysis
compgo_file <- "data/day8.compgo.up.mf.RDS"
if (file.exists(compgo_file)){
compGO <- readRDS(compgo_file)
}else{
compGO <- enrichGO(gene = sigResponse_up,
universe = bg_genes,
keyType = "ENSEMBL",
OrgDb = "org.Hs.eg.db",
ont = "MF",
qvalueCutoff = 0.05,
pAdjustMethod = "BH",
readable = TRUE)
saveRDS(compGO, compgo_file)
}
# image pdf 12 x 12
## Output results from GO analysis to a table
print("UP")
## [1] "UP"
results_up <- data.frame(compGO@result) %>% dplyr::filter(p.adjust < 0.05)
nrow(results_up)
## [1] 28
write_csv(results_up, "tables/T22.day8.GO_MF_UP.csv")
dotplot(compGO,
showCategory = 20,
title = "GO (Molecular Function) Enrichment \n Analysis for UP in pCR)",
label_format = 20,
font.size = 10)
compgo_file <- "data/day8.compgo.down.mf.RDS"
if (file.exists(compgo_file)){
compGO <- readRDS(compgo_file)
}else{
compGO <- enrichGO(gene = sigResponse_down,
universe = bg_genes,
keyType = "ENSEMBL",
OrgDb = "org.Hs.eg.db",
ont = "MF",
qvalueCutoff = 0.05,
pAdjustMethod = "BH",
readable = TRUE)
saveRDS(compGO, compgo_file)
}
results_down <- data.frame(compGO@result) %>% dplyr::filter(p.adjust < 0.05)
print("Down")
## [1] "Down"
nrow(results_down)
## [1] 0
write_csv(results_up, "tables/T22.day8.GO_MF_DOWN.csv")
if (nrow(results_down) > 0){
dotplot(compGO,
showCategory = 20,
title = "GO (Molecular Function) Enrichment \n Analysis for DOWN in pCR)",
label_format = 20,
font.size = 10)
}
bg_genes <- rownames(resResponse)
## Run GO enrichment analysis
compgo_file <- "data/day8.compgo.up.cc.RDS"
if (file.exists(compgo_file)){
compGO <- readRDS(compgo_file)
}else{
compGO <- enrichGO(gene = sigResponse_up,
universe = bg_genes,
keyType = "ENSEMBL",
OrgDb = "org.Hs.eg.db",
ont = "CC",
qvalueCutoff = 0.05,
pAdjustMethod = "BH",
readable = TRUE)
saveRDS(compGO, compgo_file)
}
# image pdf 12 x 12
## Output results from GO analysis to a table
print("UP")
## [1] "UP"
results_up <- data.frame(compGO@result) %>% dplyr::filter(p.adjust < 0.05)
nrow(results_up)
## [1] 71
write_csv(results_up, "tables/T23.day8.GO_CC_UP.csv")
dotplot(compGO,
showCategory = 20,
title = "GO (Cellular compartment) Enrichment \n Analysis for UP in pCR)",
label_format = 20,
font.size = 10)
compgo_file <- "data/day8.compgo.down.cc.RDS"
if (file.exists(compgo_file)){
compGO <- readRDS(compgo_file)
}else{
compGO <- enrichGO(gene = sigResponse_down,
universe = bg_genes,
keyType = "ENSEMBL",
OrgDb = "org.Hs.eg.db",
ont = "CC",
qvalueCutoff = 0.05,
pAdjustMethod = "BH",
readable = TRUE)
saveRDS(compGO, compgo_file)
}
results_down <- data.frame(compGO@result) %>% dplyr::filter(p.adjust < 0.05)
print("Down")
## [1] "Down"
nrow(results_down)
## [1] 0
write_csv(results_up, "tables/T24.day8.GO_CC_DOWN.csv")
if (nrow(results_down) > 0){
dotplot(compGO,
showCategory = 20,
title = "GO (Cellular compartment) Enrichment \n Analysis for DOWN in pCR)",
label_format = 20,
font.size = 10)
}
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Fedora 32 (Workstation Edition)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.12.so
##
## locale:
## [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] clusterProfiler_3.18.1 org.Hs.eg.db_3.12.0
## [3] ensembldb_2.14.1 AnnotationFilter_1.14.0
## [5] GenomicFeatures_1.42.3 AnnotationDbi_1.52.0
## [7] AnnotationHub_2.22.1 BiocFileCache_1.14.0
## [9] dbplyr_2.1.1 knitr_1.30
## [11] ggrepel_0.9.1 tximport_1.18.0
## [13] DEGreport_1.26.0 pheatmap_1.0.12
## [15] RColorBrewer_1.1-2 forcats_0.5.1
## [17] stringr_1.4.0 dplyr_1.0.5
## [19] purrr_0.3.4 readr_1.4.0
## [21] tidyr_1.1.3 tibble_3.1.1
## [23] ggplot2_3.3.3 tidyverse_1.3.1
## [25] DESeq2_1.30.1 SummarizedExperiment_1.20.0
## [27] Biobase_2.50.0 MatrixGenerics_1.2.1
## [29] matrixStats_0.58.0 GenomicRanges_1.42.0
## [31] GenomeInfoDb_1.26.7 IRanges_2.24.1
## [33] S4Vectors_0.28.1 BiocGenerics_0.36.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.1 tidyselect_1.1.0
## [3] RSQLite_2.2.7 grid_4.0.3
## [5] BiocParallel_1.24.1 scatterpie_0.1.6
## [7] munsell_0.5.0 withr_2.4.2
## [9] colorspace_2.0-0 GOSemSim_2.16.1
## [11] rstudioapi_0.13 DOSE_3.16.0
## [13] labeling_0.4.2 lasso2_1.2-21.1
## [15] GenomeInfoDbData_1.2.4 polyclip_1.10-0
## [17] mnormt_2.0.2 farver_2.1.0
## [19] bit64_4.0.5 downloader_0.4
## [21] vctrs_0.3.7 generics_0.1.0
## [23] xfun_0.19 R6_2.5.0
## [25] graphlayouts_0.7.1 clue_0.3-59
## [27] locfit_1.5-9.4 bitops_1.0-7
## [29] cachem_1.0.4 reshape_0.8.8
## [31] fgsea_1.16.0 DelayedArray_0.16.3
## [33] assertthat_0.2.1 promises_1.2.0.1
## [35] scales_1.1.1 ggraph_2.0.5
## [37] enrichplot_1.10.2 gtable_0.3.0
## [39] Cairo_1.5-12.2 conquer_1.0.2
## [41] tidygraph_1.2.0 MatrixModels_0.5-0
## [43] rlang_0.4.10 genefilter_1.72.1
## [45] GlobalOptions_0.1.2 splines_4.0.3
## [47] rtracklayer_1.50.0 lazyeval_0.2.2
## [49] broom_0.7.6 BiocManager_1.30.12
## [51] yaml_2.2.1 reshape2_1.4.4
## [53] modelr_0.1.8 backports_1.2.1
## [55] httpuv_1.6.0 qvalue_2.22.0
## [57] tools_4.0.3 psych_2.1.3
## [59] logging_0.10-108 ellipsis_0.3.1
## [61] ggdendro_0.1.22 Rcpp_1.0.6
## [63] plyr_1.8.6 progress_1.2.2
## [65] zlibbioc_1.36.0 RCurl_1.98-1.3
## [67] prettyunits_1.1.1 openssl_1.4.3
## [69] viridis_0.6.0 GetoptLong_1.0.5
## [71] cowplot_1.1.1 haven_2.4.1
## [73] cluster_2.1.0 fs_1.5.0
## [75] magrittr_2.0.1 data.table_1.14.0
## [77] DO.db_2.9 SparseM_1.81
## [79] circlize_0.4.12 reprex_2.0.0
## [81] tmvnsim_1.0-2 ProtGenerics_1.22.0
## [83] hms_1.0.0 mime_0.9
## [85] evaluate_0.14 xtable_1.8-4
## [87] XML_3.99-0.6 readxl_1.3.1
## [89] gridExtra_2.3 shape_1.4.5
## [91] compiler_4.0.3 biomaRt_2.46.3
## [93] shadowtext_0.0.8 crayon_1.4.1
## [95] htmltools_0.5.1.1 later_1.2.0
## [97] geneplotter_1.68.0 lubridate_1.7.10
## [99] DBI_1.1.1 tweenr_1.0.2
## [101] ComplexHeatmap_2.6.2 MASS_7.3-53
## [103] rappdirs_0.3.3 Matrix_1.2-18
## [105] cli_2.5.0 igraph_1.2.6
## [107] pkgconfig_2.0.3 rvcheck_0.1.8
## [109] GenomicAlignments_1.26.0 xml2_1.3.2
## [111] annotate_1.68.0 XVector_0.30.0
## [113] rvest_1.0.0 digest_0.6.27
## [115] ConsensusClusterPlus_1.54.0 Biostrings_2.58.0
## [117] rmarkdown_2.5 cellranger_1.1.0
## [119] fastmatch_1.1-0 edgeR_3.32.1
## [121] curl_4.3 quantreg_5.85
## [123] shiny_1.6.0 Rsamtools_2.6.0
## [125] rjson_0.2.20 lifecycle_1.0.0
## [127] nlme_3.1-149 jsonlite_1.7.2
## [129] viridisLite_0.4.0 askpass_1.1
## [131] limma_3.46.0 fansi_0.4.2
## [133] pillar_1.6.0 lattice_0.20-41
## [135] Nozzle.R1_1.1-1 fastmap_1.1.0
## [137] httr_1.4.2 survival_3.2-7
## [139] GO.db_3.12.1 interactiveDisplayBase_1.28.0
## [141] glue_1.4.2 png_0.1-7
## [143] BiocVersion_3.12.0 bit_4.0.4
## [145] ggforce_0.3.3 stringi_1.5.3
## [147] blob_1.2.1 memoise_2.0.0